Search

Understanding the controlling factors of soil water vapour sorption at different water activities (aw) is essential for accurate estimation of soil properties, such as cation exchange capacity (CEC) and specific surface area (SSA). The objective of this study was to identify the role of CEC and soil pore system in water vapour sorption and sorption hysteresis over a range of aw values. The CEC, SSA, pore volume, and water and nitrogen adsorption/desorption isotherms for eight soils with different clay contents and mineralogies were measured. Irrespective of aw and sorption direction, there was a significant correlation between water content and CEC, and the correlation varied with aw. The water content change was mainly related to CEC and SSAH2O for aw < ∼0.6, and to SSAN2 and pore volume for aw > ∼0.6. Similar to the forced closure phenomenon of the soil nitrogen desorption branch at a relative pressure (P/P0) of ∼ 0.45, the soil water vapour desorption branch also exhibited a sudden drop in the range of 0.2 < aw < 0.4. The water vapour sorption hysteresis for aw < ∼0.75 was mainly due to the difference in cation hydration during the adsorption and desorption process, and the hysteresis phenomena for aw > ∼0.75 was attributed to the different sizes of narrow pore necks and the connected pores.

A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near-infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

Optical remote sensing of earth surface processes commonly relies on the red, green, blue (RGB), near-infrared (NIR) and shortwave-infrared (SWIR) electromagnetic bands. Most of the optical sensors...

In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.

Among the proxy traits involved in the adaptive response of wheat to drought and other abiotic stresses, information on the QTLome governing osmotic adjustment (OA) and chlorophyll fluorescence imaging (CFI) is very limited, mainly due to the difficulty to measure these traits on a scale suitable for GWAS. In this study, OA and CFI were measured in plots of 248 elite accessions of durum wheat grown (two replicates) under a fully automated Lemnatec Field Scanalyzer (LFS) and exposed to a progressively severe drought treatment starting 5 days prior to flowering which lasted for two weeks. Leaf relative water content (RWC), chlorophyll content (SPAD) and leaf rolling (LR) were also measured. CFI was measured (i) in the dark in four time series using the LFS camera and (ii) during daytime using a light-induced fluorescence transient (LIFT) sensor mounted on a manually pushed cart. The high variability in OA and CFI among the durum accessions resulted in high to medium repeatability (h2 = 72.3 and 54.6%, respectively). At the end of the drought treatment (mean leaf RWC = 62.1%), OA and leaf RWC were positively correlated (r = 0.78). Association mapping using flowering time as covariate revealed 15 QTLs for OA (global R2 = 63.6%) as well as eight major QTL hotspots on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL and 7BS where a higher OA capacity was always positively associated with leaf RWC, SPAD and negatively associated with LR, hence indicating a beneficial effect of OA on the water status of the plant. Additionally, the comparative analysis with previous field trials showed concurrent effects for five of these hotspots on normalized difference vegetation index (NDVI), thousand kernel weight (TKW) and/or grain yield (GY), hence supporting OA as a valuable proxy for marker-assisted selection aimed at enhancing drought resistance in wheat.

Water repellency (WR) significantly affects the hydraulic behavior of soils. Although WR often is regarded as a phenomenon with implications for dry soils, it is prevalent at water contents (w) exceeding the wilting point water content. Because the measurement of the WR–w relationship is laborious, alternative more time-efficient methods are desirable to estimate parameters of the WR-w curve. Using 32 high organic carbon (OC) soils from Denmark and South Greenland, we characterized the water vapor sorption isotherms (WSIs), investigated the interrelated effects of OC and clay contents on WSIs and the WR–w relationship, and further evaluated if parameters of the WR-w curve may be derived directly from WSIs. The samples exhibited OC and clay contents ranging from 0.014 to 0.369 kg kg–1 and from 0.02 to 0.16 kg kg–1, respectively. The WSIs measured for relative humidity (RH) values between 3 and 93%, were strongly hysteretic, were OC dependent, and could be accurately characterized with the Guggenheim, Anderson, and de Boer model. Further, the WRarea and wnon parameters, derived from WR measured for several w, were well estimated with linear regressions based on OC content and multiple linear regressions based on OC and clay contents. Estimations for WRarea and wnon based on the WSI parameter wm-a were superior to OC and clay content. Finally, we established mathematical expressions that estimate WRarea or wnon from any air-dry w obtained from either the desorption or adsorption isotherms between 10 and 90% RH.

The adsorbed water content is an attractive predictor for estimation of soil specific surface area (SSA) as its measurement is less laborious and more cost effective than standard laboratory techniques. We analysed the effects of total specific surface area (SSAtot), external specific surface area (SSAex) and internal specific surface area (SSAin) on water vapour sorption on 21 soil samples, and proposed models for estimating SSAex, SSAtot and SSAin from organic carbon content and the slope of soil water vapour sorption isotherm (SL0.5) and water content (WC0.5) at a water activity of 0.5. The results indicated that the variation of correlation coefficients between water content change at 0.05 water activity interval (WCC0.05) and SSAtot with water activity was mainly due to the differences in water vapour adsorption in interlayer spaces at different water activity levels. Furthermore, in the soil water content range from ~0.001 to ~0.03 g g−1 (within the water activity range from ~0.45 to ~0.75), water vapour adsorption was related closely to SSAex. Cross-validation results on estimated SSAex, SSAtot and SSAin of the 21 samples produced root mean square error (RMSE) values less than 9.39 m2 g−1 and Nash-Sutcliffe model efficiency coefficients (E) greater than 0.93, suggesting that the proposed models provided reasonable estimates of SSA components. Thus, soil water vapour adsorption data can be applied to simultaneously estimate SSAex, SSAtot and SSAin. Variation of correlation between WCC0.05 and SSAtot with water activity depended on SSAin. Soil water vapour sorption is related closely to SSAex at water activities from ~0.45 to ~0.75. The proposed models yield reasonable estimates of SSAtot, SSAex and SSAin. Adsorbed water content can be used as a predictor for SSA estimation. Highlights: Variation of correlation between WCC0.05 and SSAtot with water activity depended on SSAin. Soil water vapor sorption relates closely to SSAex at water activities from ~0.45 to ~0.75. The proposed models yield reasonable estimates of SSAtot, SSAex, and SSAin. Adsorbed water content can be used as a predictor for SSA estimation.

The hysteretic behaviour of the dry region (2 = 0.92), except for the kaolinite-rich samples (R2 = 0.35). The SOC-rich samples that exhibited illitic clay mineralogy and similar soil texture showed a significant increase in average hysteresis with increasing organic carbon content (R2 = 0.93). For all soil samples combined, the CEC was the strongest indicator for the magnitude of water vapour sorption hysteresis. Highlights: A new index for quantification of soil vapour sorption hysteresis was proposed Large SOC and clay content increased sorption hysteresis For soil samples, dominant clay mineralogy controlled the magnitude of hysteresis Cation exchange capacity was the best predictor of hysteresis for all soil types.

Saline playas in arid and semiarid regions of the world are significant sources of unconsolidated sediments susceptible to aeolian transport. Lake Urmia in Iran, one of the largest saltwater lakes on Earth, has waned to approximately 18% of its original size due to groundwater pumping and surface water diversions. This has led to ecosystem degradation, accelerated desertification and frequent dust storms, causing public health problems. In this paper we introduce a new framework for delineation of dust source zones and estimation of dust occurrence probability based on remotely sensed land surface properties (soil moisture and vegetation cover), soil texture, wind speed, and measured dust frequencies. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the Urmia Lake basin were utilized to determine the Normalized Difference Vegetation Index (NDVI) and to estimate surface soil moisture with a recently introduced optical trapezoid model. The soil textures extracted from the SoilGrids database and wind speeds obtained from local weather stations together with the estimated surface soil moisture were found to be highly correlated with dust emission probability. When the surface soil moisture is low, wind speed is the major determinant for dust occurrence. With increasing surface soil moisture, the dust occurrence probability decreases. Soil moisture effects are more pronounced at high wind speeds. While at low wind speeds the dust occurrence probabilities for the investigated soil textures loam, clay loam and sandy clay loam are similar, at higher wind speeds the sandy clay loam texture exhibits the highest susceptibility to dust generation.

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security.

The accurate determination of land surface water fluxes at various spatiotemporal scales remains a challenge in hydrological sciences. It is intuitive that land surface net water flux (i.e., infiltration minus evapotranspiration) directly affects near-surface soil moisture. However, there exists no hydrological model suitable to calculate net water flux based on measured near-surface soil moisture data. This is a consequence of the mathematical structure of existing models that use ‘boundary conditions’ to determine ‘internal conditions’, whereas what is needed is a model amenable to use near-surface soil moisture data (an internal condition) to determine the surface water flux (a boundary condition). To pursue the idea of utilizing remotely-sensed or in situ (i.e., sensor networks) near surface soil moisture data for estimation of net water flux, we derived an analytical model via inversion of Warrick’s 1975 analytical solution to the linearized Richards’ equation for arbitrary time-varying surface flux boundary conditions. The applicability of the new analytical solution was evaluated based on actual water flux observations as well as HYDRUS-1D simulations for four vastly different sites in Arizona, California, Idaho, and Indiana. Our results demonstrate that the proposed model reasonably captures net water flux variations in natural settings, including layered and vegetated soils, especially at larger time scales (e.g., monthly). While the model works for a wide range of climatic conditions, the prediction accuracy is somewhat lower for extreme dry or wet conditions. A major advantage of the new model is that it does not require calibration, which provides an unprecedented opportunity for large scale estimation of land surface net water flux using remotely sensed near-surface soil moisture observations.

The dependence of the relationship between hygroscopic water content (θh) and clay content on water activity is not well understood. Here, we determined the correlation coefficients between θh (or the slope of soil water vapor adsorption isotherms, Svai) and clay content at nine water activity levels (i.e., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) and investigated the evolution of correlation coefficients with increasing water activity for 65 soils with mixed and illitic clay mineralogy. The correlation coefficient between θh0.1 (water content at a water activity of 0.1) and clay content was the lowest (.82) due to the differences in soil-specific hydration processes. At the water activity range from 0.1 to 0.6, the Svai showed stronger correlations (r =.91–.93) with clay content than θh0.1 because the multilayer sorption controlled by van der Waals force is related to clay content. When water activity increased from 0.1 to 0.6, the observed strong correlation between Svai and clay content resulted in an increase of correlation coefficient between θh and clay content from.82 to.96. Compared with the correlation (r =.96) between θh and clay content at the beginning of condensation process, a weaker correlation (r =.87–.92) between Svai and clay content was observed during condensation process, which led to a slight strengthening of the correlation between θh and clay content at high water activities (>0.6). This was explained by the different clay particle properties and water activity thresholds for initiation of condensation.

Measurements of the soil water characteristic (SWC) and unsaturated hydraulic conductivity [K(h)] curves, which are at the core of modeling flow and transport processes in porous media, are laborious and prone to experimental errors. To overcome some of the current experimental limitations, we examined the potential feasibility of shortwave infrared (SWIR) imaging of water imbibition into dry soil during a controlled laboratory experiment in conjunction with inverse numerical modeling to determine the wetting SWC and the K(h) function. To generate time series of high‐resolution surface moisture maps, the imaged surface reflectance was converted to surface soil moisture via a recently developed physical radiative transfer model. The moisture time series were then used to parameterize the HYDRUS 2D/3D numerical code for forward simulations. The optimization was performed with simulated annealing in MATLAB that was linked to HYDRUS 2D/3D to automate the inversion process. The obtained SWC wetting curves were subsequently compared with Tempe cell and Dewpoint PotentiaMeter measurements. Although further research and refinements of the proposed method are needed, the results of this exploratory study obtained for a broad range of soil textures are promising and demonstrate the potential feasibility of the proposed approach for rapid estimation of soil hydraulic properties.

Numerical simulation of three-dimensional water flow and solute transport in containerized variably saturated soilless substrates with complex hydraulic properties and boundary conditions necessitates high-resolution discretization of the spatial and temporal domains, which commonly leads to several million nodes requiring numerical evaluation. Even today’s computing prowess of workstations is not adequate to tackle such problems within a reasonable timeframe, especially when numerous realizations are required to optimize the geometry, substrate properties, and irrigation and fertigation management of soilless plant growth modules. Hence, the parallelization of the numerical code and utilization of high performance computing (HPC) are essential. Here, we adapted and applied the ParSWMS parallelized code that is amenable to solving the 3D Richards equation for water flow and the convection-dispersion equation for solute transport subject to linear solute adsorption. The code was modified to allow for nonlinear equilibrium solute adsorption with new boundary conditions and applied to simulate water flow and nitrogen and phosphorus transport in containerized soilless substrates. Multi-solute transport simulations with the modified Linux ParSWMS code were first performed on a workstation and referenced to the Windows-based HYDRUS (2D/3D) numerical code. After confirming the agreement between the modified ParSWMS code and HYDRUS (2D/3D), various preconditioners and iterative solvers were evaluated to find the computationally most efficient combinations. The performance of the modified ParSWMS code and its stability were compared to HYDRUS (2D/3D) simulations for three soilless substrates consisting of horticultural perlite, volcanic tuff, and a volcanic tuff/coconut coir mixture. Considering the solute mass balance error as a stability measure, ParSWMS outperformed HYDRUS (2D/3D). Moreover, simulations with the modified ParSWMS code were about 22% faster than simulations with HYDRUS (2D/3D) on the workstation. Tests of the modified ParSWMS on two HPC clusters with 28 and 94 cores revealed a potential computational speedup of 94% relative to the HYDRUS (2D/3D) simulations performed on the workstation.

The pore–solid interface and its characteristics play a key role in chemical interactions between minerals in the solid soil matrix and the liquid in pore space and, consequently, solute transport in soils. Specific surface area (SSA), typically measured to characterize the pore–solid interface, depends not only on the particle size distribution (PSD) but also on particle shapes and surface roughness. In this note, we investigate the effects of surface roughness and probing molecule size on SSA estimation, use concepts from fractals, and theoretically estimate SSA from PSD and the water retention curve (WRC). The former is used to characterize the particle sizes and the latter to approximately quantify the pore–solid interface roughness by determining the surface fractal dimension. To evaluate our approach, we use five Washington and 21 Arizona soils for which both PSDs and WRCs were accurately measured over a wide range of particle sizes and matric potentials. Comparison with the experiments show that the proposed method estimates the SSA reasonably well, with RMSE = 16.8 and 30.1 m2 g–1 and average relative error = –56 and –35% for the Washington and Arizona datasets, respectively.

A number of environmental, agronomic and engineering applications require knowledge of the Atterberg limits (liquid limit, LL; plastic limit, PL) and the plasticity index, PI of soils. The tedious and costly nature of standard experimental methods, as well as challenges with measurement repeatability motivated the development of regressions as well as more sophisticated techniques to estimate the Atterberg limits from other properties such as clay content, cation exchange capacity (CEC), and soil specific surface area. The amount of water adsorbed to particle surfaces at relative humidity (RH) < 95% is intimately linked to these soil properties, which suggests that hygroscopic water content (wh) may be a better predictor of the Atterberg limits. The present study (i) proposes regression models that estimate the LL, PL, and PI from wh at different relative humidity values ranging from 10 to 90% and considering water sorption hysteresis, and (ii) compares the performance of the models to other models that comprise clay, silt and organic carbon contents and CEC. For model development, wh was measured by water adsorption and desorption for 168 soil samples that varied widely in terms of geographic origin, clay mineralogy, and soil organic carbon content. The LL and PL were determined with the drop cone penetrometer and rolling device, respectively. Regression models were developed for both sorption directions for nine RH values between 10 and 90%. For 44 independent soil samples, the models estimated LL, PL and PI accurately (e.g., for desorption wh measured at 90% RH, RMSE and r2 values were 6.43% & 0.89; 3.95% & 0.83 and 6.69% & 0.79, respectively). There was no clear effect of sorption direction on the estimation accuracy. The wh determined at higher RH tended to better estimate the Atterberg limits compared to that measured at lower RH. The wh models were superior in estimating LL and PL compared to models that were based on clay content and organic carbon or CEC. For the PI, the models based on CEC performed slightly better than the wh models. Thus, a single measure of wh can provide reliable estimates of the Atterberg limits and PI.

Many arid and semiarid regions of the world face serious water shortages that are projected to have significant adverse impacts on irrigated agriculture and create unprecedented challenges for providing food and water security for the rapidly growing human population in a changing global climate. Consequently, there is a momentous incentive to shift to more resource-efficient soilless greenhouse production systems. Though there is considerable empirical and theoretical research devoted to specific issues related to control and management of soilless culture systems, a comprehensive approach that quantitatively considers relevant physicochemical processes within containerized soilless growth modules is missing. An important first step towards development of advanced soilless culture management strategies is a comprehensive characterization of hydraulic and physicochemical substrate properties. In this study we applied state-of-the-art measurement techniques to characterize six soilless substrates and substrate mixtures [i.e., coconut coir, perlite, volcanic tuff, perlite/coconut coir (50/50 vol.-%), tuff/coconut coir (70/30 vol.-%), and Growstone&reg, /coconut coir (50/50 vol.-%)] that are used in commercial production in Israel and the United States. The measured substrate properties include water retention characteristics, saturated hydraulic conductivity, packing and particle densities, as well as phosphorus and ammonium adsorption isotherms. In addition, integral water availability and integral energy parameters were calculated to compare investigated substrates and provide valuable information for irrigation and fertigation management.

Tropical rain forests are greatly dependent on water supply and are highly efficient in water cycling. Soil infiltration rates as well as tree transpiration rates are high in these often seasonally dry ecosystems. Both deforestation and climate change have been shown to cause drought stress in tropical forests, the former through the increase of runoff and reduction in evapotranspiration, the latter mainly through the reduction in precipitation and transpiration.Although great efforts have been made to determine the ecosystem and species responses to variable water supply, many processes determining how tree species in tropical ecosystems impact and are impacted by the water cycle (water uptake and redistribution, and stem storage) remain poorly understood. Water movement through trees, as measured by a D2O pulse label in the rainwater, was found to be high variable and species dependent in a previous experiment in the Biosphere 2 tropical rainforest (Evaristo et al. 2019). We hypothesized that differential rooting depth and/or stem water storage could be the main causes for the difference in water label transport through the trees.Our study is part of a large-scale experiment in the Biosphere 2 tropical forest that uses isotope labeling (13C and D) to trace C- and water-cycle processes underpinning ecosystem responses to drought from a molecular to an ecosystem-scale level. Here, we focus on the water cycling of this ecosystem and how it is impacted by controlled drought and rewetting conditions. Detailed continuous measurements of both the water pools (soil and stem) and movement (stems, atmospheric fluxes) will be used to determine individual tree (including different species) and whole ecosystem responses to drought. These data will be presented in light of their implications for tropical forest water movement and drought vulnerability.ReferenceEvaristo J, Kim M, van Haren J, Pangle LA, Harman CJ, Troch PA, McDonnell JJ, (2019) Characterizing the fluxes and age distribution of soil water, plant water and deep percolation in a model tropical ecosystem. Water Resources Research, 55(4), 3307-3327.

The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near-infrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.

Osmotic adjustment (OA) is a major component of drought resistance in crops. The genetic basis of OA in wheat and other crops remains largely unknown. In this study, 248 field-grown durum wheat elite accessions grown under well-watered conditions, underwent a progressively severe drought treatment started at heading. Leaf samples were collected at heading and 17 days later. The following traits were considered: flowering time (FT), leaf relative water content (RWC), osmotic potential (ψs), OA, chlorophyll content (SPAD), and leaf rolling (LR). The high variability (3.89-fold) in OA among drought-stressed accessions resulted in high repeatability of the trait (h2 = 72.3%). Notably, a high positive correlation (r = 0.78) between OA and RWC was found under severe drought conditions. A genome-wide association study (GWAS) revealed 15 significant QTLs (Quantitative Trait Loci) for OA (global R2 = 63.6%), as well as eight major QTL hotspots/clusters on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL, and 7BS, where a higher OA capacity was positively associated with RWC and/or SPAD, and negatively with LR, indicating a beneficial effect of OA on the water status of the plant. The comparative analysis with the results of 15 previous field trials conducted under varying water regimes showed concurrent effects of five OA QTL cluster hotspots on normalized difference vegetation index (NDVI), thousand-kernel weight (TKW), and/or grain yield (GY). Gene content analysis of the cluster regions revealed the presence of several candidate genes, including bidirectional sugar transporter SWEET, rhomboid-like protein, and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein, as well as DREB1. Our results support OA as a valuable proxy for marker-assisted selection (MAS) aimed at enhancing drought resistance in wheat.

Experimental evidence points to an intimate link between soil reflectance, R, and particle/aggregate diameter, D. Based on this strong correlation, various statistical methods for remote and proximal sensing of soil texture and hydraulic properties have been developed. In this paper, we derive a more fundamental and physically-based analytical radiative transfer model that yields a closed-form functional R(D) relationship for dry soils. Despite several simplifying assumptions, the proposed model shows good agreement with measured spectral reflectance (350–2500 nm) data of six soils covering a broad range of textures, colors, and mineralogies. The proposed S-shaped R(D) function resembles cumulative particle and pore size distributions as well as the soil water characteristic function. These analogies may potentially lead to new avenues for developing novel physical models for extracting important soil properties from remotely sensed reflectance data.

The OPtical TRApezoid Model (OPTRAM) has recently been proposed for estimation of soil moisture using only optical remote sensing data. The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (STR) and can be parameterized universally (i.e., a single calibration for a given area) based on the pixel distribution within the STR-Normalized Difference Vegetation Index (NDVI) trapezoidal space. The main motivation for this study was to evaluate how the universal parameterization of OPTRAM works for long periods of time (e.g., several decades). This is especially relevant for uncovering the soil moisture and agricultural drought history in response to climate change in different regions. In this study, MODIS satellite observations from 2001 to 2017 were acquired and used for the analysis. Cosmic-ray neutron (CRN) soil moisture data, collected with the COsmic-ray Soil Moisture Observing System (COSMOS) at five different sites in the U.S. covering diverse climates, soil types, and land covers, were applied for evaluation of the MODIS-OPTRAM-based soil moisture estimates. The OPTRAM soil moisture estimates were further compared to the Soil Moisture Active and Passive (SMAP) (L-band), the Soil Moisture Ocean Salinity (SMOS) (L-band), and the Advanced AScatterometer (ASCAT) (C-band) soil moisture retrievals. OPTRAM soil moisture data were also analyzed for potential monitoring of agricultural drought through comparison of the OPTRAM-based Soil Water Deficit Index (OPTRAM-SWDI) with the widely-applied Crop Moisture Index (CMI). Evaluation results indicate that OPTRAM-based soil moisture estimates provide overall unbiased RMSE and R between 0.050 and 0.085 cm3 cm−3 and 0.10 to 0.70, respectively, for all investigated sites. The performance of OPTRAM is comparable with the ASCAT retrievals, but slightly less accurate than SMAP and SMOS. OPTRAM and the three microvave satellites captured CRN soil moisture temporal dynamics very well for all five investigated sites. A close agreement was observed between the OPTRAM-SWDI and CMI drought indices for most selected sites. In conclusion, OPTRAM can estimate temporal soil moisture dynamics with reasonable accuracy for a range of climatic conditions (semi-arid to humid), soil types, and land covers, and can potentially be applied for agricultural drought monitoring.

Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.

Near-surface soil conditions (i.e., moisture and temperature) moderate mass and energy exchange at the soil–atmosphere interface. While remote sensing offers an effective means for mapping near-surface moisture content across large areas, in situ measurements, targeting those specific remotely sensed soil depths, are poorly understood and high-resolution near-surface measurement capabilities are lacking. Time domain reflectometry (TDR) is a well-established, accurate measurement method for soil dielectric permittivity and moisture content. A TDR array was designed to provide centimeter-resolution measurements of near-surface soil moisture. The array consists of nine stainless steel TDR rods spaced 1 cm apart, acting as waveguide pairs to form eight two-rod TDR probes in series. A critical aspect of the design was matching the spacing of the coaxial cable–TDR rod transition to avoid unwanted reflections in the waveforms. The accuracy of the TDR array permittivity measurement (±1 permittivity unit) was similar to that of conventional TDR as verified in dielectric liquids. Electric field numerical simulations showed minimal influence of adjacent rods during a given rod-pair measurement. The evaporation rate determined by the TDR array compared well with mass balance data in a laboratory test. Near-surface soil moisture profile dynamics were monitored at centimeter-depth resolution using the TDR array in a field experiment where volumetric moisture content estimates (0–8 cm) were within 2% of conventional three-rod TDR probes averaging across 0 to 8 cm and from 1- to 3-cm depths

For simulating flow in heterogeneous porous media it is computationally more efficient to define an equivalent effective (i.e., upscaled) medium rather than considering detailed spatial heterogeneities. In this paper, the effective unsaturated hydraulic conductivity (K) of soils exhibiting random variability, layering, or both is calculated based on numerical simulations of steady-state evaporation from a shallow water table. It is demonstrated that the effective K of randomly-varied coarse-textured soils generally falls between the harmonic and geometric means of the unsaturated hydraulic conductivities of the constituting soils. Layering and random variability when occurring concurrently magnify each other’s effects on effective K. As a result, the higher the degree of heterogeneity, the lower the effective K. Therefore, neglecting either random spatial variability or layering in numerical simulations can lead to significant overestimation of water flow in soils.

The soil water retention curve (SWRC) is the most fundamental soil hydraulic function required for modelling soil–plant–atmospheric water flow and transport processes. The SWRC is intimately linked to the distribution of the size of pores, the composition of the solid phase and the soil specific surface area. Detailed measurement of the SWRC is impractical in many cases because of the excessively long equilibration times inherent to most standard methods, especially for fine textured soil. Consequently, it is more efficient to predict the SWRC based on easy-to-measure basic soil properties. In this research we evaluated a new two-stage approach developed recently to predict the SWRC based on measurements for disturbed repacked soil samples. Our study involved undisturbed structured soil and took into account the effects of bulk density, organic matter content and particle-size distribution. Independently measured SWRCs for 171 undisturbed soil samples with organic matter contents that ranged from 3 to 14% were used for model validation. The results indicate that consideration of the silt and organic matter fractions, in addition to the clay fraction, improved predictions for the dry-end SWRC. The dry-end results revealed that the smallest matric potential at hypothetical ‘zero-water-content’ varies between −45 000 and −125 000 m (pF 6.65–7.1) even for soils with similar clay mineralogy. The sensitivity analysis of the two-stage approach indicated that predicted SWRC results are more sensitive to bulk density than to organic matter content or soil texture. For the soils studied, the two-stage approach showed reasonable agreement with measured data, with a root mean square error of 0.04 cm3 cm−3 for the matric potential range from pF 1 to pF 6.6. Highlights Is a new approach to modelling the soil water retention (SWR) curve applicable to structured soil? The model accurately predicts the full SWR curve with an RMSE of 0.04 cm3 cm−3. Prediction accuracy of the wet part of the SWR curve was sensitive to variation in bulk density. The pF at zero water content, which affects the prediction of dry SWR, ranged from 6.65 to 7.10.

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm−3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.

Standard measurement methods for soil clay content, including the hydrometer and pipette methods, are laborious and difficult to repeat. This has motivated the application of proxy methods such as estimation of clay content from soil water vapor sorption isotherms. Previous studies have revealed that vapor sorption-based clay estimates are inaccurate for soils dominated by swelling or highly weathered non-swelling clays such as kaolinite. In this study, we evaluate an existing vapor sorption-based clay estimation model for soils dominated by either illite (IL), smectite (SM), or kaolinite (KA) clay minerals, and propose modifications to account for high SM and KA contents. Compared to the pipette method, the clay content obtained from the existing water sorption model at 50% relative humidity (RH) was accurate for the IL samples, but significantly overestimated (RMSE = 23.7%; ME = 19.7%) or underestimated (RMSE = 28.8%; ME = −17.0%) clay content for SM and KA rich soils, respectively. The proposed modification involves correcting the estimated clay content with a “slope factor”, S for RH values ranging from 30 to 90% for both adsorption and desorption. For SM samples, S averaged 0.76 and 0.72 for adsorption and desorption, respectively, and for KA samples, S ranged from 2.26 to 1.19 and followed a polynomial relationship with RH. Comparison of the estimated clay content from the modified models showed markedly improved estimation of the measured clay content (for RH of 90%, RMSE = 8.0%; ME = 2.6% for SM samples and RMSE = 9.6%; ME = −5.7% for KA samples).

Recent technological innovations offer new opportunities for soil moisture characterization and monitoring from the pedon to global scales.

The hysteretic behaviour of the dry region of the soil water characteristic, which is of essence for accurate characterization and modelling of bio-physicochemical soil processes under dry conditions, is well documented. However, knowledge about how to best quantify water vapour sorption hysteresis and about the effects of soil properties on hysteresis is limited. To overcome this knowledge gap, we proposed an index (average hysteresis) for quantifying sorption hysteresis and evaluated its applicability based on measured sorption isotherms of four source clay minerals and 155 soil samples. Furthermore, the effects of clay mineralogy, clay content, soil organic carbon, and cation exchange capacity (CEC) on the magnitude of sorption hysteresis were investigated. For the clay minerals, kaolinite did not exhibit hysteretic behaviour, illite, and Ca-smectite showed some hysteresis, while Na-smectite was strongly hysteretic. For the soil samples, the average hysteresis increased with increasing clay content for the different clay mineral groups, and with increasing organic carbon content (R2 = 0.93) for similarly textured soil samples. The average hysteresis, corrected for clay content, was strongly affected by the dominant clay mineralogy. Regardless of the dominant clay mineralogy, clay content or organic matter content, the CEC was a good predictor of the average hysteresis for all the soil samples.